Abstract: An HOD device, comprising: a framework; covering material, covering the frame work; at least one conductive region, provided on or in the covering material; wherein the conductive region is coupled to a capacitance detection circuit or a predetermined voltage level. The HOD device can be a vehicle control device such as a steering wheel. The conductive region comprises conductive wires which can be threads of the covering material. By this way, the arrangements of the conductive wires can be changed corresponding to the size or the shape of the frame work or any other requirements. Also, the interference caused by unstable factors can be improved since the conductive wires can be coupled to a ground source of the vehicle to provide a short capacitance sensing path.

Abstract: A method for evaluating a risk of a subject getting a specific disease includes steps of: storing a reference database that contains original parameter sets; selecting target alleles from an SNP profile derived from genome sequencing data of a subject; selecting target parameter sets from among the original parameter sets; calculating, for each of the target parameter sets, a race factor based on a global risk allele frequency and a group-specific risk allele frequency included in the target parameter set; calculating a genetic factor based on statistics, global reference allele frequencies, the race factors for the target parameter sets, and numbers of chromosomes in homologous chromosome pairs included in the target parameter sets; calculating a citation factor based on numbers of citation times included in the target parameter sets; and calculating a risk score based on the genetic factor and the citation factor.

Abstract: A method of manufacturing a semiconductor device includes forming a first layer of a first planarizing material over a patterned surface of a substrate, forming a second layer of a second planarizing material over the first planarizing layer, crosslinking a portion of the first planarizing material and a portion of the second planarizing material, and removing a portion of the second planarizing material that is not crosslinked. In an embodiment, the method further includes forming a third layer of a third planarizing material over the second planarizing material after removing the portion of the second planarizing material that is not crosslinked. The third planarizing material can include a bottom anti-reflective coating or a spin-on carbon, and an acid or an acid generator. The first planarizing material can include a spin-on carbon, and an acid, a thermal acid generator or a photoacid generator.

Abstract: The present disclosure relates to an integrated chip including a dielectric structure over a substrate. A first capacitor is disposed between sidewalls of the dielectric structure. The first capacitor includes a first electrode between the sidewalls of the dielectric structure and a second electrode between the sidewalls and over the first electrode. A second capacitor is disposed between the sidewalls. The second capacitor includes the second electrode and a third electrode between the sidewalls and over the second electrode. A third capacitor is disposed between the sidewalls. The third capacitor includes the third electrode and a fourth electrode between the sidewalls and over the third electrode. The first capacitor, the second capacitor, and the third capacitor are coupled in parallel by a first contact on a first side of the first capacitor and a second contact on a second side of the first capacitor.

Abstract: A system for recognizing abnormal activity of human body using wearable electronic device and mixed reality technology, includes a first wearable electronic device, a plurality of second wearable electronic devices, at least one camera, and a judgement module. The system is suitable to be applied in any type of work environment, so as to monitor and determine whether a user (i.e., an operator or an employee) exhibits abnormal activities by referring a document of standard operating procedures (SOP), an operating manual and a document of safety operation standard (SOS), to reduce a rate of user mishandling, thereby achieving the enhancement of work efficiency and productivity.

Abstract: An image enhancement method includes the following steps. Separating an original image signal into a high frequency image signal and a low frequency image signal by performing a wavelet transform through a first transformation circuit. Separating the high frequency image signal into a high frequency separation signal and a high-intermediate frequency separation signal by performing the wavelet transform through a second transformation circuit. Separating the low frequency image signal into a low-intermediate frequency separation signal and a low frequency separation signal by performing the wavelet transform. Enhancing high frequency separation signal to generate an enhanced high frequency separation signal configured to enhance the sharpness of an image.

Abstract: A system for distinguishing identities based on nose prints of animals contains: an input end, a database, an identification unit, and an output end. The input end is configured to input image data. The database includes multiple animal identity data which are actual nose prints data, actual body information, actual face data, and identity information of the animals. The identification unit is electrically connected with the input end, the database, and an output end. The identification unit includes multiple identification programs configured to analyze the image data, thus obtaining compared nose prints data, compared body information, and compared face data of the animals. The compared nose prints data, the compared body information, and the compared face data of the animals are compared with the actual nose prints data, the actual body information, and the actual face data of the animal identity data respectively.

Abstract: A system for distinguishing identities based on nose prints of animals contains: an input end, a database, an identification unit, and an output end. The input end is configured to input image data. The database includes multiple animal identity data which are actual nose prints data, actual body information, actual face data, and identity information of the animals. The identification unit is electrically connected with the input end, the database, and an output end. The identification unit includes multiple identification programs configured to analyze the image data, thus obtaining compared nose prints data, compared body information, and compared face data of the animals. The compared nose prints data, the compared body information, and the compared face data of the animals are compared with the actual nose prints data, the actual body information, and the actual face data of the animal identity data respectively.

Abstract: An image enhancement method includes the following steps. Separating an original image signal into a high frequency image signal and a low frequency image signal by performingperforming a wavelet transform through a first transformation circuit. Separating the high frequency image signal into a high frequency separation signal and a high-intermediate frequency separation signal by performingperforming the wavelet transform through a second transformation circuit. Separating the low frequency image signal into a low-intermediate frequency separation signal and a low frequency separation signal by performingperforming the wavelet transform. Enhancing high frequency separation signal to generate an enhanced high frequency separation signal configured to enhance the sharpness of an image.

Abstract: A multi-microcontroller system, comprising a master microcontroller and a plurality of slave microcontrollers; wherein the master microcontroller is connected with the slave microcontrollers respectively via a network bridge; the network bridge forms a first communication part with a first memory interface and a first SPI interface, the plurality of first communication parts are connected with the master microcontroller and the slave microcontrollers, respectively; the network bridge is primarily responsible for processing transmission of control signals and data between the master microcontroller and the slave microcontrollers, and serves as a temporary storage area for common memory such that the states of the slave microcontrollers are put under automated management, the addresses of the slave microcontrollers are designated, and memory blocks are allocated.

Abstract: A multi-microcontroller system, comprising a master microcontroller and a plurality of slave microcontrollers; wherein the master microcontroller is connected with the slave microcontrollers respectively via a network bridge; the network bridge forms a first communication part with a first memory interface and a first SPI interface, the plurality of first communication parts are connected with the master microcontroller and the slave microcontrollers, respectively; the network bridge is primarily responsible for processing transmission of control signals and data between the master microcontroller and the slave microcontrollers, and serves as a temporary storage area for common memory such that the states of the slave microcontrollers are put under automated management, the addresses of the slave microcontrollers are designated, and memory blocks are allocated.

Abstract: An image interpolation method and an image interpolation device and an image apparatus using the image interpolation method are provided. The image interpolation method uses a probabilistic neural network model to perform an adaptive interpolation on an image. The image interpolation method includes the following steps. Firstly, plural reference points neighboring an interpolation point are selected. Then, an anisotropic Gaussian function value of each reference point of the plural reference points is obtained according to an edge direction angle, a horizontal smoothing parameter, a vertical smoothing parameter and a distance between each reference point and the interpolation point. Afterwards, a statistics method is performed to integrate and compute the anisotropic Gaussian function values of the plural reference points, thereby obtaining an interpolation value of the interpolation point.

Abstract: An image distortion correction method and an image distortion correction device are provided. The image distortion correction method uses a neural network model to perform a correcting operation on an original image so as to obtain a correction image with plural correction points. Firstly, a position coordinate of the correction point is inputted into the neural network model, so that a first direction coordinate correction amount is outputted from the neural network model. Then, the position coordinate of the correction point is inputted into the neural network model, so that a second direction coordinate correction amount is outputted from the neural network model. Afterwards, a pixel value of the original image corresponding to the first direction coordinate correction amount and the second direction coordinate correction amount is used as a pixel value of the correction point.

Abstract: An image-capturing system with a dual lens camera provided in the present invention includes a first lens, a second lens, an actuating module, a control unit, and an image processing unit. The actuating module is utilized to rotate the first lens and the second lens. The control unit controls the actuating module to rotate the first lens and the second lens such that a first visibility region overlaps a second visibility region to define an overlapping range which has a first mode and a second mode according to a size of the overlapping region. The image processing unit stitches the first image of the first lens and the second image of the second lens for forming an ultra wide-angle image in the first mode; the image processes unit processing the first image and the second image into a stereoscopic image in the second mode.

Abstract: An image distortion correction method and an image distortion correction device are provided. The image distortion correction method uses a neural network model to perform a correcting operation on an original image so as to obtain a correction image with plural correction points. Firstly, a position coordinate of the correction point is inputted into the neural network model, so that a first direction coordinate correction amount is outputted from the neural network model. Then, the position coordinate of the correction point is inputted into the neural network model, so that a second direction coordinate correction amount is outputted from the neural network model. Afterwards, a pixel value of the original image corresponding to the first direction coordinate correction amount and the second direction coordinate correction amount is used as a pixel value of the correction point.

Abstract: An image interpolation method and an image interpolation device and an image apparatus using the image interpolation method are provided. The image interpolation method uses a probabilistic neural network model to perform an adaptive interpolation on an image. The image interpolation method includes the following steps. Firstly, plural reference points neighboring an interpolation point are selected. Then, an anisotropic Gaussian function value of each reference point of the plural reference points is obtained according to an edge direction angle, a horizontal smoothing parameter, a vertical smoothing parameter and a distance between each reference point and the interpolation point. Afterwards, a statistics method is performed to integrate and compute the anisotropic Gaussian function values of the plural reference points, thereby obtaining an interpolation value of the interpolation point.

Abstract: A device for auto baud rate detection includes an edge trigger, a counter, at least one register and a central processing unit. The edge trigger detects whether a potential of a transmitted signal is raising or falling. The counter is connected to the edge trigger and uses the edge trigger to count pulse widths of the transmitted signal. The register is connected to the counter and stores the pulse widths. The central processing unit is connected to the register, compares the pulse widths so as to identify a minimal pulse width, and calculates a baud rate according to the minimal pulse width by using a probability density function. Thereby, the device is advantageous for lowering hardware costs, increasing use flexibility, reducing configuration limitation and ensuring accurate baud rate detection.

Abstract: A wireless sensor actuator network (WSAN) is provided, which includes: at least one sensor, for sensing situations of surrounding environments to generate a transferring condition, the transferring condition corresponding to a transferring condition serial number; at least one actuator, driven by a driving signal; and a gateway, for receiving the transferring condition of the sensor corresponding to the transferring condition serial number, transferring an active state serial number to a transferring state serial number which meets the transferring condition according to an encoding table of the gateway, and executing functions called by the transferring state serial number to generate the driving signal for driving the actuator.

Abstract: The present invention discloses an optical input device, which comprises a display panel having a displaying area functioning as an interface for detecting a position of an object; a backlight module providing light sources for the display panel; and at least one image sensor arranged behind the backlight module and capturing a positional image, which is formed on the displaying area by an object reflecting the light emitted by the light sources. The present invention not only can decrease the thickness of the optical input device but also can reduce the complexity of the optical system.

Abstract: A method uses a hybrid neural network including a self organizing mapping neural network (SOM NN) and a, back-propagation neural network (BP NN) for color identification. In the method the red, green and blue (RGB) of color samples are input as features of training samples and are automatically classified by way of SOM NN. Afterwards, the outcomes of SOM NN are respectively delivered to various BP NN for further learning; and the map relationship of the input and the output defines the X,Y, Z corresponding the x, y and z values of a coordinate system of the standard color samples of RGB and IT8. By way of the above learning structure, a non-linear model of color identification can be set up. After color samples are self organized and classified by SOM NN network, data can be categorized in clusters as a result of characteristic difference thereof.